Bibliographic record
Abstract
Prepectoral prosthetic breast reconstruction has become an acceptable option for women following mastectomy. Benefits include no animation deformity, absence of pectoralis major muscle spasm, and less pain and discomfort. Important aspects of prepectoral reconstruction include working with breast surgeons that are adept at performing an optimal mastectomy. Tissue perfusion and reasonable thickness of the mastectomy are critical components of success. Tissue necrosis, infection, and delayed healing can lead to reconstructive failure. Given the risks and benefits of this procedure, questions regarding indications, patient selection, and specific details related to technique remain because there is no consensus. Whether it is safe to perform prepectoral reconstruction in obese or previously irradiated patients is controversial. The use of acellular dermal matrix is common but not universal. The amount of acellular dermal matrix used is variable, with success being demonstrated with the partial and total wrap techniques. Device selection can vary but is critical in the prepectoral setting. Postoperative care and the management of adverse events are important to understand and can impact surgical and aesthetic outcomes. This article provides current approaches, recommendations, and an algorithm for prepectoral breast reconstruction with an emphasis on patient selection, immediate versus delayed prepectoral reconstruction, specific technical details, and postoperative management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".